Determinants of beekeeping adoption by smallholder rural households in Northwest Ethiopia
Bibliographic record
Abstract
There is an enormous potential for beekeeping practices to generate income, create jobs, and alleviate poverty. However, in Ethiopia, there are many constraints that hinder rural households to expand and adopt beekeeping practices. The objective of this study was to analyze the determinants of beekeeping adoption in Northwest Ethiopia. To achieve the objective, cross-sectional data were collected from 369 rural households and analyzed using a nonlinear econometric (binary logistic regression) model. The maximum likelihood estimation results revealed that sex, marital status, household size, and the educational status of the household head, number of extension visits, membership in a farmers’ association, and access to credit were the statistically significant variables determining beekeeping adoption in the study area. The beekeeping constraints that had statistically significant influence on beekeeping adoption were grouped as marketing, natural, and financial. To reap the benefits from the huge potential of honeybee colonies, the government of Ethiopia and other associated actors and stakeholders should work together to solve the constraints faced by rural households in adopting beekeeping practices that could result in improving their livelihoods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".